99 citations · 157 across the 39 of their papers we have counts for
13 papers · 1 filter
The Era of Vibe Tapeout: Dark Age or Golden Age for Analog Circuit Design?
Kentaro Yoshioka
LLM agents now write their own schematic and layout generators, run EDA tools, read the results, and fix the design. This "vibe tapeout" is becoming practical for analog circuits a…
Understanding Clustering in Slot Attention via Particle Dynamics
Vasudev Joy, Rajat Rasal, Avinash Kori +2
Studying attention through the lens of interacting particle dynamics has shown how token clustering can emerge from the underlying dynamics. We extend this perspective to slot atte…
Density-Robust Spherical Coordinates from Persistent Cohomology
Nick Nordwald, Inés García-Redondo, Anthea Monod
Persistent cohomology provides a principled framework for constructing nonlinear coordinates that reflect the topology of data. However, these topological coordinates can be severe…
Tracking Representation Dynamics in Large Language Models with Persistent Homology
Naman Malhotra, Jay Ambadkar, Abhinav Gupta +4
Large language models are commonly aligned through supervised fine-tuning, yet little is known about how their internal representations evolve during this process. We study alignme…
Generative Modeling on Metric Graphs via Neural Optimal Transport
Alessandro Micheli, Yueqi Cao, Anthea Monod +1
We introduce, to our knowledge, the first deep generative modeling framework for probability distributions continuously supported on compact metric graphs. Given source and target…
Non-Archimedean Polydisc Spaces and Applications to Optimisation
Paul Lezeau, Yiannis Fam, Anthea Monod +1
We propose a new framework for optimisation over non-Archimedean spaces inspired by Berkovich geometry. Specifically, we introduce polydisc spaces, which consists of products of cl…
Topological Signatures of Grokking
Yifan Tang, Qiquan Wang, Inés García-Redondo +1
We study the grokking phenomenon through the lens of topology. Using persistent homology on point clouds derived from the embedding matrices of a range of models trained on modular…
Feature Starvation as Geometric Instability in Sparse Autoencoders
Faris Chaudhry, Keisuke Yano, Anthea Monod
Sparse autoencoders (SAEs) are used to disentangle the dense, polysemantic internal representations of large language models (LLMs) into interpretable, monosemantic concepts. Howev…
Entropic Riemannian Neural Optimal Transport
Alessandro Micheli, Silvia Sapora, Anthea Monod +1
Many machine learning problems involve data supported on curved spaces such as spheres, rotation groups, hyperbolic spaces, and general Riemannian manifolds, where Euclidean geomet…
Trajectory-Restricted Optimization Conditions and Geometry-Aware Linear Convergence
Faris Chaudhry, Anthea Monod, Keisuke Yano
Linear convergence of first-order methods is typically characterized by global optimization conditions whose constants reflect worst-case geometry of the ambient space. In high-dim…
On the Uniqueness of Fréchet Means for Polytope Norms
Roan Talbut, Andrew McCormack, Anthea Monod
Fréchet means are a popular type of average for non-Euclidean datasets, defined as those points which minimise the average squared distance to a set of data points. We consider the…
Breaking Symmetry Bottlenecks in GNN Readouts
Mouad Talhi, Arne Wolf, Anthea Monod
Graph neural networks (GNNs) are widely used for learning on structured data, yet their ability to distinguish non-isomorphic graphs is fundamentally limited. These limitations are…
Riemannian Neural Optimal Transport
Alessandro Micheli, Yueqi Cao, Anthea Monod +1
Computational optimal transport (OT) offers a principled framework for generative modeling. Neural OT methods, which use neural networks to learn an OT map (or potential) from data…